Dr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\\n\\n
Seeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\\n\\n
Over these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\\n\\n
We are excited about the present, and we look forward to sharing many more successes in the future.
\\n\\n
Thank you all for being part of the journey. 5,000 times thank you!
\\n\\n
Now with 5,000 titles available Open Access, which one will you read next?
Preparation of Space Experiments edited by international leading expert Dr. Vladimir Pletser, Director of Space Training Operations at Blue Abyss is the 5,000th Open Access book published by IntechOpen and our milestone publication!
\n\n
"This book presents some of the current trends in space microgravity research. The eleven chapters introduce various facets of space research in physical sciences, human physiology and technology developed using the microgravity environment not only to improve our fundamental understanding in these domains but also to adapt this new knowledge for application on earth." says the editor. Listen what else Dr. Pletser has to say...
\n\n\n\n
Dr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\n\n
Seeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\n\n
Over these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\n\n
We are excited about the present, and we look forward to sharing many more successes in the future.
\n\n
Thank you all for being part of the journey. 5,000 times thank you!
\n\n
Now with 5,000 titles available Open Access, which one will you read next?
\n'}],latestNews:[{slug:"stanford-university-identifies-top-2-scientists-over-1-000-are-intechopen-authors-and-editors-20210122",title:"Stanford University Identifies Top 2% Scientists, Over 1,000 are IntechOpen Authors and Editors"},{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"},{slug:"oiv-awards-recognizes-intechopen-s-editors-20201127",title:"OIV Awards Recognizes IntechOpen's Editors"},{slug:"intechopen-joins-crossref-s-initiative-for-open-abstracts-i4oa-to-boost-the-discovery-of-research-20201005",title:"IntechOpen joins Crossref's Initiative for Open Abstracts (I4OA) to Boost the Discovery of Research"},{slug:"intechopen-hits-milestone-5-000-open-access-books-published-20200908",title:"IntechOpen hits milestone: 5,000 Open Access books published!"},{slug:"intechopen-books-hosted-on-the-mathworks-book-program-20200819",title:"IntechOpen Books Hosted on the MathWorks Book Program"}]},book:{item:{type:"book",id:"8783",leadTitle:null,fullTitle:"Sustainable Sewage Sludge Management and Resource Efficiency",title:"Sustainable Sewage Sludge Management and Resource Efficiency",subtitle:null,reviewType:"peer-reviewed",abstract:"Creating decent living conditions for all people while decoupling economic growth from the increasing use of virgin resources and environmental impacts is the major challenge of this millennium. 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1. Introduction
Our knowledge of the world is derived from our perceptions, and an individual’s ability to navigate his/her surroundings or engage in activities of daily living such as walking, reading, watching TV, and driving, naturally relies on his/her ability to process sensory information. Thus deficits in visual abilities, due to disease, injury, stroke or aging, can have significant negative impacts on all aspects of an individual’s life. Likewise, an enhancement of visual abilities can have substantial positive benefits to one’s lifestyle. While a central concern of Ophthalmology is to address diseases of the eye (e.g. ocular impairments), an equally important component of vision is how the brain processes information that is received from the eye.
Vision is a synergistic effect of eye sensing and brain processing mechanisms. Vision can be compared to a satellite dish with the eye representing the dish or the sensor receiving aspect of the system. The eye/sensor captures light signals and transfers these signals to the brain, which is our visual processor. Through a series of brain processing stages, the image is processed by perceptual, higher order cognitive, and then motor systems resulting in decisions and responses. Thus, vision deficits can be due to eye mechanics, brain processing problems or both. Research of Perceptual Learning provides some answers and solutions for brain processing issues. This research demonstrates that the adult visual system is sufficiently plastic to ameliorate effects of low vision, including amblyopia [1], presbyopia [2], macular degeneration [3], stroke [4, 5], and late-life recovery of visual function [6]. Likewise, normal sighted individuals have the potential to further improve their vision through Perceptual Learning. These visual gains are related to brain function improvement (plasticity).
In this chapter we review the field of Perceptual Learning and its promise to achieve better outcomes in clinical practice. The significance of the development of effective, low-cost therapies to treat brain-based low vision can be life-altering for millions of people worldwide.
2. Perceptual Learning
Perceptual Learning (PL) refers to a long lasting improvement in perceptual abilities as a result of experience. Research of this topic has undergone tremendous development over the last 30 years. Plasticity in the sensory systems was previously thought to occur only in early development. This view has been substantiated by studies of a “critical period”. The concept of a critical period states some processes develop early in life, and do not develop, or develop to a lesser degree, later in life. For example, classic experiments done in kittens demonstrate a critical period for ocular dominance where early patching enables inputs from the open eye to take over much of primary visual cortex, however, in adult cats patching has little impact on connectivity [7, 8]. This data was used to support the hypothesis that the low-level sensory stages need to consistently process primitive sensory features; such as in vision, orientation, spatial frequency, and local motion. However, studies of perceptual learning show that even in adults, perceptual abilities can be sharpened with repeated exposure or training. For example, perceptual abilities, including elementary processes (e.g., contrast sensitivity [9] and visual acuity [1, 2, 10, 11]) can be strengthened through appropriate training approaches.
Perceptual Learning is exemplified by long-lasting improvement on simple but difficult perceptual tasks. The effects of perceptual learning have been shown to last months, even years [12-14]. The field of perceptual learning is one of growing interest largely due to the fact training on visual perception can be highly specific to the trained visual features and can give clues into the stages of processing at which learning occurs. For example, a series of studies conducted by Schoups and colleagues [15, 16] showed that training subjects (human and monkey) on an orientation discrimination task around a particular reference orientation yielded learning effects that failed to transfer to other stimulus orientations at the trained location or that same orientation at a different retinotopic location. They postulated that these learning effects were consistent with plasticity in neurons residing in primary visual cortex, which show a high degree of both retinotopic and orientation specificity. Physiological studies by this group confirmed these predictions with the demonstration of plasticity of orientation tuning across early visual cortex [15, 17]. Consistent with this, numerous behavioral studies show perceptual learning can be highly specific to a wide range of trained stimulus features including retinotopic location [18, 19], visual orientation [15, 20] and direction [12, 19], among others. Likewise, many neuroscientific studies provide evidence of sensory plasticity in all stages of visual processing through single-unit recording in monkeys [15, 21-23] and fMRI signal changes in humans [24-26].
An important caveat is that psychophysical studies of perceptual learning are only a rough tool for making inferences of the underlying neural structures. Accordingly, physiological studies demonstrating low-level perceptual learning typically fail to explain the magnitude of the behavioral changes [27] and some models of perceptual learning demonstrate that channel reweighting in the readout of sensory areas can account for some aspects of perceptual learning specificity without requiring plasticity in primary sensory areas [28-31]. Other studies have found plasticity in higher-level visual areas that were originally hypothesized to be lower level features [14, 32-34]. For instance, Law and Gold [33] failed to find plasticity in monkey area middle temporal cortex (MT), but found learning effects in a later area (lateral intraparietal cortex; LIP) that largely explained behavioral changes after training in a visual motion task. Likewise, learning in visual area V4 has been found to be more robust than that in V1 [17, 22, 32, 35, 36]. Also, some aspects of learning could be taking place in other brain regions, an interesting case was recently found in which the superior colliculus [37, 38] and frontal brain areas [39] develop tuning to motion directions after extensive training. While the exact locus of visual plasticity in a given study is often an issue of significant controversy, as a whole these studies give indication that plasticity is likely occurring at all stages of visual processing; although with a distribution that varies across tasks and training paradigms.
3. Perceptual Learning as a method to improve vision
Software programs integrating perceptual learning are being utilized more frequently to optimize outcomes in specific visual conditions, both in research/clinical studies and commercially. Using computer generated visual stimuli presented in repetitive patterns, users interact with visual stimuli and the training process induces neurological visual system plasticity and patient benefits. The observed benefits can include increased neuro-adaptation to new visual environments, improved contrast sensitivity and increases in spatial or visual acuity. Recent research provides examples of perceptual learning techniques that result in visual improvements for a variety of visual conditions.
4. Amblyopia
Amblyopia results in a lack of stereovision and poor vision in the amblyopic eye (even after the optics of the eye, and misalignment between the eyes, are corrected). Amblyopia impacts 2-3% of the population and is conventionally considered untreatable in adults. The gold standard for treating amblyopia is to restore stereovision. To accomplish this, (1) cortical processing of the amblyopic eye needs to be strengthened, (2) suppression from the non-amblyopic eye needs to be lessened, and (3) binocular integration needs to process correctly in the visual system. Amblyopia is typically treated only children, where traditional approaches focusing on patching or the use of atropine in the non-amblyopic eye, with no treatment attempted in adults due to a believed lack of plasticity.
However, recently perceptual learning paradigms have been found to be effective in improving acuity and stereopsis in adults, and in children where traditional patching was unsuccessful [40-42]. More recently there has been focus on binocular interactions in amblyopia with push-pull trainings that put the eyes in competition successfully lessening suppression in the amblyopic eye [43, 44], or binocular integration, which trains the two eyes to work better together [45]. Perceptual learning techniques, or more recently commercial video games [46], have also been found to reduce crowding [47], and improve spatial frequency and contrast discrimination, which also transferred to untrained spatial frequencies in adults with amblyopia [48]. Together these approaches have led to numerous examples which demonstrate substantial benefits in vision in the amblyopic eye and provide great promise for future perceptual learning based treatment approaches.
5. Age-related Macular Degeneration (AMD)
Age-related Macular Degeneration (AMD) is the leading cause of central low vision in adults. The prevalence of AMD increases after age 50, and is expected to affect nearly three million Americans by 2020 (The Eye Diseases Prevalence Research Group, 2004). AMD patients suffer a retinal disorder in which photoreceptors are damaged or displaced, resulting in visual field loss, spatial distortions to the visual field, and impairments of acuity and contrast sensitivity. Despite a range of treatments to arrest the progress of AMD, damage to the retina cannot be reversed, resulting in a need for effective visual training therapies. There are a number of studies that show both functional learning in the development of preferred looking points [49-51] and cortical reorganization in foveal responses to peripheral stimuli [3, 52]. Difficulty reading is a common complaint in AMD patients due to the central vision loss. Recently, Chung [53] demonstrated that perceptual learning could improve reading speed in these patients after training. Additionally, Liu et al [54] trained profoundly visually impaired individuals (including AMD, glaucoma, retinitis pigmentosa, and other conditions) on a visual search task. Search speed and accuracy improved after training, effects that remained for at least one month.
While, there are limited perceptual-learning studies in AMD patients and it is unclear the extent to which normally occurring reorganizations are driven through use-dependent mechanisms [55], there is significant potential benefit to applications of perceptual learning in AMD.
6. Age related visual decline
It is well documented that vision declines with age [56]. Deficits are seen in many aspects of vision including eye optics, luminance, contrast, orientation, motion processing, form, scene and depth perception, and optical flow. Recent research has found perceptual learning can improve visual performance in older individuals. For example, after training on a visual discrimination task older participants improved up to the same performance level of untrained college age participants, with improvements lasting at least 3 months after training [57]. Additionally, older individuals improved on a motion detection task (either drifting sine wave gratings or random dot motion), and learning transferred to the untrained motion type [58].
The most common age related visual deficit is presbyopia. Presbyopia is a progressive normal aging process where the elasticity of the lens of the eye is reduced [59]. Although the decrease in elasticity of lens starts at birth, this condition, noticed by most people in their early 40’s, manifests as a reduced ability to focus on nearby stimuli and a reduction of contrast sensitivity [2]. There is no cure for presbyopia. Typical treatments are reading glasses or multifocal lenses, contact lenses or more recently, surgery like the Laser-Assisted in Situ Keratomileusis (LASIK). While treatments do exist, their use can be inconvenient for some as many treatments are not ideal for all daily activities, or can represent a significant challenge to others. All treatment options of presbyopia induce further reduction in optical contrast [60, 61]. Additionally, the use of multifocal lenses (common form of therapy) introduces unnatural viewing conditions where the point of focus depends upon gaze angle and optical aberration in transition regions. Of note, encouraging research from Polat and colleagues [62] suggest perceptual learning can ameliorate the effects of presbyopia, and in some cases, enable mild presbyopes to read without glasses. After training on a contrast detection task using Gabor patches of a range of spatial frequencies participants improved near vision acuity, reading speed, contrast detection and discrimination without changing the optics of the eye.
In general, perceptual learning also shows great promise for conditions for which there are no standard treatments. These include the conditions mentioned above as well as other low-vision conditions such as glaucoma, night vision deficits, retinitis pigmentosa, low myopia, diabetes, etc., as well as a complement to medical technologies such as LASIK, intraocular lens implantations, retinal implants, and other treatments that yield improvements in vision but for which suboptimal cortical processing leaves patients without the full potential benefit of the optical improvements that they’ve gained.
7. Applying Perceptual Learning in ophthalmology practice
As eyecare technology advances, so do patient expectations. Optimizing outcomes and managing expectations is a consistent challenge in clinical practice. Eye practitioners agree that good vision promotes healthier lifestyles [63], aids educational processes [64] and visual impairment is detrimental to life itself [65, 66]. An international online survey sponsored by Bausch and Lomb called NSIGHT (Needs, Symptoms, Incidence, Global Eye Health Trends) found that “seeing better” was the most important consideration for choosing vision products. Patients prefer vision related products that emphasize vision improvement, therefore therapy goals need to involve products and methods to improve vision to a maximum. Clinicians pay tremendous attention to the “eye side” of the visual system but emerging research shows that we need to pay more attention to the neurological or brain processing aspect of vision which lies outside of conventional treatments.
Vision training is not a new phenomenon in Ophthalmology, eye exercises have been used for hundreds of years. The most common vision training procedures, currently and traditionally, focus on exercising the optics of the eye (e.g. flippers, prisms, and alternating fixation between distances). However these techniques lack reliable evidence of success [67]. Alternatively, recent perceptual learning based software intervention programs for eye related disorders have shown great promise and there is increasing evidence of their efficacy. Historically reserved for database and assessment tool functions, eyecare software is expanding rapidly into therapeutic interventions and treatment. Computer software is now finding real world use in the visual world of binocular disorders, amblyopia, neuro-rehabilitation and visual enhancement. Researchers and software developers are encouraged by research showing that specific software use actualizes the potential of the visual system and translates into real life gains. Therefore, the science of improving brain processing is not only relevant but utilizing these tools to treat these disorders can prove life changing. With that said, not all computer programs produce positive visual benefits and the results derived from training can vary from individual to individual. In other words, a simple software program may create entertainment but does not necessarily create real world vision improvement.
Here, we highlight a few perceptual training products currently on the market that doctors are using to help patients.
GlassesOff is an iOS app that enhances contrast sensitivity. Studies have found that contrast sensitivity decreases in disease states but also diminishes with age [68, 69]. Therefore, presbyopia is a combination of both decreased accommodation but also decreased contrast sensitivity. GlassesOff reports several studies on their website related to their product. A recent study found acuity and contrast sensitivity improved in presbyopes after using GlassesOff for approximately 3 months [62]. A perceptual learning technique called “collinear facilitation” is utilized to strengthen neural connections and reduce visual noise. The reduction of visual noise then increases visual clarity. It claims a 90% success rate for dismissing glasses for reading. For more information see glassesoff.com.
Nova Vision is a home computer training program used to reduce visual field deficits and provide visual benefits to patients suffering from the effects of stroke and traumatic brain injury. Nova studies show that visual field can be expanded an additional 5% over time [70]. According to another study, 75% of the trainees reported improved mobility after training [71]. Ideal training consists of using the technology twice daily for 30 minutes a session for approximately 6 months. One can find more information at Novavision.com.
RevitalVision/NeuroVision was developed to aid doctors in the treatment of amblyopia, presbyopia and cataract surgery. RevitalVision has also been used to enhance the vision of sports athletes. Depending on the visual condition, the program requires 40 training sessions for the treatment of amblyopia and 20 sessions for other conditions. Three sessions are recommended weekly and each session lasts up to 30 minutes. Stated benefits include increased contrast sensitivity, enhanced visual acuity, reduction in haloes and improved sense of night vision [72]. The program is used at home on a Windows PC. Some doctors are prescribing the technology to patients to accelerate adaptation to altered visual states due to cataract or LASIK surgery. For more information see Revitalvision.com
ULTIMEYES is a recent application that works on a home computer (Windows PC, Apple Mac) or iOS or Android to train vision. Implementing recent neuroscience advances, ULTIMEYES training combines paired visual and auditory stimuli and was designed in a video-game like way, to make the training more enjoyable for the patient. Benefits include improved contrast sensitivity, enhanced visual acuity, increased night vision and a reduction of haloes. Training time requires 4 weekly sessions for a total of 30 sessions, and each training session takes approximately 25 minutes to complete. A recent study found improved acuity and contrast sensitivity in normal sighted individuals after 2 months of ULTIMEYES training [10]. ULTIMEYES has also been used in the treatment of low vision conditions including presbyopia, amblyopia, post-LASIK rehabilitation, and post-cataract surgery rehabilitation (especially effective for multifocal patients), and with athletes for improved sports performance [11]. For more information see Ultimeyesvision.com.
This is not an exhaustive list of available technologies but gives the reader an idea of commercially available products using software technology to tune brain processing and create better visual performance.
8. Principles of Perceptual Learning
An important question in evaluating studies of PL, is how do we know what to learn? In other words, how does a neural system know which information is behaviorally relevant and which is not? Given that plasticity can occur in adult sensory systems, there must be some mechanism that gates what is learned (i.e. to control what aspects are allowed and what aspects are restricted). In the following sections, we review different mechanisms and approaches that help guide perceptual learning.
8.1. Attention
Attention refers to a set of fundamental mental process that selectively modulates the processing of relevant information over irrelevant information; it informs decisions, guides memory processes, and our executive control to direct resources to act upon the world. A common belief is that perceptual learning cannot occur without persistent and intensive attention to the feature to be learned [73]. Profound learning effects are often present for task-relevant features but are typically absent or very limited for the task-irrelevant and unattended features. For example, Ahissar and Hochstein [74] found no or little transfer of learning effects between two tasks that involved judgments on different stimulus attributes (either orientation of local elements or global shape) of the same stimuli. It was also reported that the ability of subjects to discriminate the orientation of a line did not improve when the brightness rather than orientation of the line was attended [75]. Additionally, a single-unit recording study in monkeys found neuronal plasticity manifested as a change in the orientation tuning curves of V1 cells with receptive fields overlapping the spatial location of the training task. No plasticity was found for cells with receptive fields overlapping the location of task-irrelevant stimuli presented at a different location from those relevant to the task [15]. While in the next section we’ll discuss how attention to stimuli is not actually required to achieve on those stimuli, nonetheless attention plays an important role in selecting what we do (and do not) learn.
8.2. Reinforcement
Theories of reinforcement learning show that rewards and punishment sculpt when and what we learn. At these times reinforcement signals are released to better learn aspects of the environment (even those for which the organism is not consciously aware) that are predictive or co-vary with the event. For example, in a natural environment a target (e.g., a predator) to which one needs to direct attention is usually presented in the same or similar context. Thus, gaining higher sensitivity to features in such a context may lead one to more easily notice that he/she is in the environment in which a target tends to appear and to better recognize the target [e.g. 76].
Recent research demonstrates the fundamental importance of reinforcement processes in guiding perceptual learning. For example, the research paradigm of “task-irrelevant perceptual learning” shows that sensory plasticity occurs without attention being directed to the learned stimuli, and even for those that participants are not aware [19, 77-88]. Seitz and Watanabe [84] found that a sensitivity enhancement occurred as the result of temporal-pairing between the presentation of a subliminal, task-irrelevant, motion stimulus and a task-target. In this experiment, four different directions of motion were presented an equal number of times during the exposure stage, but a single direction of interest was consistently paired (temporally preceded and then overlapped) with the task-targets. Learning was found only for the motion-direction that was temporally-paired with the task-targets, not for the other motion-directions. Similar results were obtained when the luminance contrast of the dots (100% coherence) was made so low that the subjects did not notice the presentation of the motion stimuli [81]. These results suggest that task-irrelevant perceptual learning does not occur as a result of purely passive exposure, but that the irrelevant feature must be related to task performance. These results have led to the idea that plasticity is gated by confluence between a spatially diffusive task-related signal and a task-irrelevant feature signal [79]. Later research confirmed this idea by demonstrating that task-irrelevant perceptual learning can arise through pairing a stimulus with a liquid reward [80].
Seitz and Watanabe [79] suggested a model of perceptual learning where learning results from interactions between spatially diffusive task-driven signals and bottom-up stimulus signals. Namely, that learning is gated by behaviorally relevant events (rewards, punishment, novelty, etc). At these times reinforcement signals are released to better learn aspects of the environment (even those for which the organism is not consciously aware) that are predictive or co-vary with the event. By now, task-irrelevant perceptual learning has been shown to be a robust learning phenomenon that generalizes to a wide range of stimulus features, for example, motion processing [19], orientation processing [89], critical flicker fusion thresholds [82, 83], contour integration [90], auditory formant processing [91], and phonetic processing [92]. Importantly, task-irrelevant perceptual learning produces learning effects that are often as strong, and sometimes stronger, than learning effects produced through direct training [91, 93]. While the phenomenon of task-irrelevant perceptual learning has been studied in most detail in the case of low-level perceptual learning, recent research has identified a high-level, fast form, of task-irrelevant perceptual learning (fast-task-irrelevant perceptual learning) [94-101]. In this fast-task-irrelevant perceptual learning paradigm, participants conducted target detection tasks (looking for a target, letter, color, or word among a series of distractors), while also memorizing other stimuli (images, pictures) that were consistently paired with the stimuli of the target-detection task. Similar to task-irrelevant perceptual learning for low-level perceptual learning, visual memory was enhanced for stimuli that were paired with the targets of the target-detection task. Thus task-irrelevant perceptual learning is arguably a basic mechanism of learning in the brain that spans multiple levels of processing and sensory modalities.
While we have discussed attention and reinforcement as separate processes, this distinction may be overly simplistic (e.g. [79, 86]). For example, the orienting of attention, in the direction of the target-arrow, has been linked with the acetylcholine neuromodulatory system [102]. The same neuromodulatory system has been suggested to have an important role in learning: some studies indicate that a reduction of the cholinergic input reduces cortical plasticity [103] and impairs learning [104-106]. However, other neuromodulatory systems, such as dopamine and norepinephrine have also been linked to both attention [107, 108] and to learning [109, 110]. Indeed, these three neuromodulators (acetylcholine, norepinephrine, and dopamine) have been linked to the three attentional systems described by Posner and Petersen (1990): the alerting network that involves temporal cueing and the maintenance of an alert state (norepinephrine; [111-113]; the orienting network that spatially selects information from sensory input (acetylcholine; [102]; and the executive control network that resolves conflict among responses (dopamine; [114]). These studies indicate that attention and reinforcement are deeply interrelated and that a good training approach should aim to direct both attention and reinforcement in a manner to promote learning.
8.3. Applying rules of synaptic plasticity
At the cellular level, it is widely accepted that the process of synaptic plasticity underlies learning and memory. Synaptic plasticity is the ability of the strength of the connections between synapses to change, strengthening or weakening the connections of existing neurons to modulate the effectiveness of their communication. Bliss and Lomo discovered a method to experimentally induce a persistent synaptic plasticity termed long-term potentiation (LTP) [115]. By inducing brief high frequency electrical stimulation in the perforant pathway of anaesthetized rabbits and recording in the dentate gyrus they discovered an increase of excitatory post-synaptic potentials (EPSPs) over baseline response that lasted up to 10 hours. Conversely, long-term depression (LTD) is induced by persistent low frequency electrical stimulation, resulting in weakened synaptic connections.
Recent research has established that non-invasive exposure-based stimulation protocols can be applied to the sensory systems and result in plasticity of the corresponding sensory cortices. Passive high frequency stimulation (HFS) (20 Hz) of the fingertip resulted in the behavioral improvement of a 2-point discrimination task, and low frequency stimulation (LFS) (1 Hz) decreased performance on this task [116]. Additionally, improvements on the behavioral task after HFS was correlated with cortical reorganization as assessed by mapping somatosensory evoked potentials. This effect was abolished by oral application of an NMDA receptor antagonist, indicating this effect shares similar requirements to cellular LTP and long-term memory formation as identified in the animal model [117]. Using a visual stimulation protocol Beste and colleagues [118] demonstrated behavioral changes on a change-detection task. Here, two bars were presented where a change could occur in the luminance of one bar, the orientation of one bar, the luminance and orientation of the same bar, or the luminance of one bar and the orientation of the other bar. The participants had to report a change in luminance, and ignore a change in orientation. The orientation change in the last condition was highly distracting, and made the luminance detection more difficult. A visual stimulation protocol consisted of alternating black and white bars flashing at either a high (20 Hz) or low (1 Hz) frequency with the goal of increasing or decreasing luminance saliency. The authors found a high frequency visual stimulation protocol improved the behavioral outcome on the detection task tested up to 10 days after induction. Conversely, a low frequency LTD-like protocol impaired performance. These studies of exposure-based learning provide a clear connection between the animal model and the human system, and suggest that approaches based upon knowledge of synaptic plasticity can be applied to improved perception in humans.
8.4. Multisensory facilitation
The human brain has evolved to learn and operate optimally in natural environments in which behavior is guided by information integrated across multiple sensory modalities. Crossmodal interactions are ubiquitous in the nervous system and occur even at early stages of perceptual processing [119-123]. Until recently, however, all studies of perceptual learning focused on training with one sensory modality. This unisensory training fails to tap into natural learning mechanisms that have evolved to optimize behavior in a multisensory environment. Recent research shows that subjects trained with auditory-visual stimuli exhibit a faster rate of learning and a higher degree of improvement than found in subjects trained in silence [124, 125]. Critically, these benefits of multisensory training are even found for perceptual tests without auditory signals. In other words, multisensory training facilitates unisensory learning. While, to date, most vision training procedures either don’t include sounds as part of the task (other than as feedback) or include sounds that are not coordinated with visual stimuli, the advantage of multisensory training over visual-alone training is substantial; reducing the number of sessions required to reach asymptote by ~60%, while also raising the maximum performance [126]. We suggest that having complementary information about the target objects come from different sensory modalities allows the senses to work together to facilitate learning.
8.5. Promoting transfer of learning
Classically, a translational barrier to perceptual learning has been its high degree of specificity to trained stimulus features [127]; such as orientation [20], retinal location [128] or even the eye of training [80, 129]. For example training with a single visual stimulus at a single screen location can result in learning that is specific to that situation. While such studies have been informative regarding the mechanisms of learning, specificity limits therapeutic benefits.
Recent research suggests methods of how this “curse of specificity” can be overcome. Approaches that depart from the most simple training approaches, such as those using multi-stimulus training [130, 131] and off-the-shelf video games [132, 133] show a greater generalization of learning. For example, the recently developed technique of ‘double training’ found that the specific learning effects found in their paradigms can show broad transfer when more than one stimulus attribute is trained at a time. Xiao, Zhang [131] trained participants on the Vernier discrimination task at a specific orientation at a specific location in the visual field, which normally yields location and orientation specific learning effects [129]. But when they subsequently trained a second orientation at a different spatial location, they found that the training induced changes for the second orientation transferred to the first location. Such findings of broad location transfer undermine the argument that this learning is due to plasticity in retinotopic visual areas.
There exist a growing number of studies that address how specificity, or its opposite, transfer, is controlled by different factors. In a discrimination task, Jeter, Dosher, Petrov and Lu [134] showed that transfer was observed in low-precision transfer tasks while specificity was observed in high-precision transfer tasks. Then, Jeter, Dosher, Liu and Lu [135] showed that specificity was the result of an extensive training, confirming more classical results [20, 128, 136], while a substantial transfer was observed at early in the training. Interestingly, another study, reported by Aberg, Tartaglia and Herzog [137] presented a series of experiments showing, in one hand, that the number of trials per session influenced the overall improvement of the participant’s performance, and in another hand, the transfer depended on the number of trials presented during each session, not the total number of trials. Zhang et al., [138] showed a peripheral orientation discrimination task transferred to new locations only after a pre-test was given to participants. These studies add to the double-training studies that show transfer after training multiple features or at multiple locations [130, 131]. Together these studies show that many factors (extent of training, blocking of trials, precision of training stimuli, diversity of training set, etc), influence the transfer of learning.
9. Conclusion
While extant applications of perceptual learning to Ophthalmology show great promise, a limitation of modern perceptual learning research is that learning is studied in very specific ways, focusing on one particular stimulus or factor. This narrow focus has limited understanding of the multiple learning factors that are present in natural settings and how these factors interact to determine the speed and nature of learning. We suggest a new paradigm of integrating perceptual learning methodologies into a coordinated approach that achieves a more comprehensive form of perceptual learning than typically studied in the lab. For example the approach used in the ULTIMEYES program combines many factors that are known to promote neural plasticity and generalization of learning [10, 11]. Furthermore, findings that playing off-the-shelf video games can improve vision [132, 133, 139] suggests another avenue of research where principles derived from video games should be combined with those from the field of perceptual learning to create an enriching user experience that encourages compliance with treatment while effectively optimizing how the brain process its ocular inputs.
\n',keywords:null,chapterPDFUrl:"https://cdn.intechopen.com/pdfs/46616.pdf",chapterXML:"https://mts.intechopen.com/source/xml/46616.xml",downloadPdfUrl:"/chapter/pdf-download/46616",previewPdfUrl:"/chapter/pdf-preview/46616",totalDownloads:1498,totalViews:216,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:0,dateSubmitted:"September 19th 2013",dateReviewed:"February 12th 2014",datePrePublished:null,datePublished:"September 3rd 2014",dateFinished:null,readingETA:"0",abstract:null,reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/46616",risUrl:"/chapter/ris/46616",book:{slug:"ophthalmology-current-clinical-and-research-updates"},signatures:"Jenni Deveau, Gary Lovcik and Aaron R. Seitz",authors:[{id:"145770",title:"Prof.",name:"Aaron",middleName:null,surname:"Seitz",fullName:"Aaron Seitz",slug:"aaron-seitz",email:"aseitz@ucr.edu",position:null,institution:{name:"University of California, Riverside",institutionURL:null,country:{name:"United States of America"}}},{id:"169889",title:"Dr.",name:"Jenni",middleName:null,surname:"Deveau",fullName:"Jenni Deveau",slug:"jenni-deveau",email:"jsdeveau@gmail.com",position:null,institution:null},{id:"169890",title:"Dr.",name:"Gary",middleName:null,surname:"Lovcik",fullName:"Gary Lovcik",slug:"gary-lovcik",email:"GBSJG5@aol.com",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Perceptual Learning",level:"1"},{id:"sec_3",title:"3. Perceptual Learning as a method to improve vision ",level:"1"},{id:"sec_4",title:"4. Amblyopia ",level:"1"},{id:"sec_5",title:"5. Age-related Macular Degeneration (AMD) ",level:"1"},{id:"sec_6",title:"6. Age related visual decline",level:"1"},{id:"sec_7",title:"7. Applying Perceptual Learning in ophthalmology practice",level:"1"},{id:"sec_8",title:"8. Principles of Perceptual Learning ",level:"1"},{id:"sec_8_2",title:"8.1. Attention",level:"2"},{id:"sec_9_2",title:"8.2. Reinforcement",level:"2"},{id:"sec_10_2",title:"8.3. Applying rules of synaptic plasticity",level:"2"},{id:"sec_11_2",title:"8.4. Multisensory facilitation",level:"2"},{id:"sec_12_2",title:"8.5. Promoting transfer of learning",level:"2"},{id:"sec_14",title:"9. Conclusion",level:"1"}],chapterReferences:[{id:"B1",body:'Levi, D.M. and R.W. Li, Perceptual learning as a potential treatment for amblyopia: a mini-review. Vision Res, 2009. 49(21): p. 2535-49.'},{id:"B2",body:'Polat, U., Making perceptual learning practical to improve visual functions. Vision Res, 2009. 49(21): p. 2566-73.'},{id:"B3",body:'Baker, C.I., et al., Reorganization of visual processing in macular degeneration. 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Vision Res, 2009. 49(16): p. 2087-94.'},{id:"B138",body:'Zhang, T., et al., Decoupling location specificity from perceptual learning of orientation discrimination. Vision Res, 2010. 50(4): p. 368-74.'},{id:"B139",body:'Green, C.S. and D. Bavelier, Action-video-game experience alters the spatial resolution of vision. Psychol Sci, 2007. 18(1): p. 88-94.'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Jenni Deveau",address:null,affiliation:'
Department of Psychology, University of California – Riverside, Riverside, CA, USA
'},{corresp:null,contributorFullName:"Aaron R. Seitz",address:"aseitz@ucr.edu",affiliation:'
Department of Psychology, University of California – Riverside, Riverside, CA, USA
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1. Chronic pain and fibromyalgia
1.1 The burden associated with chronic pain
Chronic pain is defined as “a pain that persists past the normal time of healing” ([1], p. 4). In practice, chronic pain is defined as a pain that lasts for more than 3–6 months [1, 2]. With an estimated prevalence up to 40%, chronic pain is regarded as a major health problem with approximated direct and indirect costs reaching to 5% of the gross national product in western European countries [3]. Also in term of prevalence, chronic pain represents an important public health issue. A recent epidemiological survey indicated that almost one in five Europeans report having experienced moderate to severe pain in the last month and at least twice a week [3]. Chronic pain significantly decreases individuals’ health status and quality of life [4] and is linked with a wide range of physical and mental problems such as sleep disorders, depression, anxiety disorders, and alcohol or substance abuse, either as antecedent conditions or as consequences of the development of pain [5]. The fact that the number of people suffering from a chronic pain condition is steadily rising [6], in spite of the overall improving standards of health care, emphasizes the urgent need for novel insights informing better diagnosis, prevention and treatment of patients with chronic pain.
1.2 The specific case of Fibromyalgia
Fibromyalgia syndrome (FMS) is a chronic, painful musculoskeletal disorder characterized by widespread pain, accompanied by a broad spectrum of associated somatic and psychological manifestations, including fatigue, sleep disturbances, stiffness, anxiety and cognitive dysfunction [7, 8]. The current diagnostic criteria of FMS emphasize the behavioral and psychological aspects of the disease and are based on self-reported evaluation of symptoms [9]. This is an important change compared to the previous diagnostic criteria that required tender point examination [8], while the new criteria are essentially based on self-reported symptoms. The development of new diagnostic criteria for FMS is related to the evolution of the understanding of the underlying pathophysiology of this disorder [10]. While the old criteria conceptualized FMS as peripheral musculoskeletal condition, the new criteria account better for the role of the central nervous system (CNS) in the etiology of FMS. In addition, they simplify the diagnosis in primary care and integrate the diversity of symptoms (somatic and behavioral) associated with FMS [10] better.
The population prevalence of FMS in industrialized countries has been reported to range from 0.5 to 4% [11], with a ratio of 3.5% in women to 0.5% in men [12]. FMS is one of the most prevalent chronic pain conditions [12]. Like other chronic pain conditions, FMS often leads to disability, affective disturbance and poor quality of life and is also associated with high direct and indirect disease related costs [13]. The etiology of FMS is widely unknown; and this disorder remains very difficult to treat. However, accumulated evidence over the past years suggests that a wide range of factors that could potentially underlie the disorder, including dysfunctions of the central CNS and autonomic nervous systems, neurotransmitters, hormones, immune system, external stressors, psychiatric aspects and others [14]. Although there is increasing evidence for changes in the CNS, FMS is differentiated from neuropathic pain as there is no evidence for a primary lesion or disease of the somatosensory system in FMS [15]. It has been hypothesized however that FMS and neuropathic pain phenomena may be variations of the same condition [16, 17] with many common features such as precipitation or aggravation by stress, as well as complaining about similar symptoms such as tingling, numbness, cutaneous hyperalgesia or pain attacks [18]. Finally, the recent evidence for impaired small fiber function in FMS patients also points towards a neuropathic nature of pain in FMS [19].
1.3 Challenges for the treatment of FMS
Despite this high clinical significance, the neural correlates and the interaction between psychological and neurobiological processes in the pathophysiology of FMS are still poorly understood, which in turn makes the development of treatment strategies difficult. At pharmacological level, three medications have been approved by the FDA for the treatment of fibromyalgia [20]: one anti-epileptic drug (pregabalin) and two antidepressive drugs (duloxetine and milnacipran). Interestingly, all of them directly act on the CNS. However, recent research indicates that current pharmacological treatments are not really effective in the reduction of pain or improvement in function in patients with FMS, and there is still a lack of effective drugs for the treatment of FMS over time [21]. Furthermore, the current evidence-based guidelines for the treatment of patients with FMS are inconsistent [22]. Finally, recent meta-analyses conclude that optimal treatment interventions should include components aimed at enhancing adaptive cognitive and behavioral responses [23, 24], and large improvements have been observed with treatment plans that include non-pharmacologic interventions [25]. This is in line with the current international guidelines that recommend aerobic exercise, cognitive-behavioral therapy (CBT), and multicomponent treatment as first choice for the care of FMS patients [22]. These conclusions are also in agreement with the current state of research concerning the treatment of chronic pain in general. For chronic pain also, most of the available medications show poor efficacy, are accompanied by severe side effects with chronic use, or, in the case of opioids, may lead to dependence or addiction [26]. In addition, chronic pain is commonly associated with comorbid affective disorders (e.g. anxiety, depression) and cognitive deficits (e.g. memory impairment), suggesting on one hand critical involvement of higher order neural brain processing [27], and on the other hand the necessity to develop specific interventions targeting the comorbid mental disorder the mood and cognitive dysfunctions as well (see for instance [28].
This chapter will therefore focus on specific neural changes associated with chronic pain in general, and with FMS in particular, that could bring new advances in the development of efficient treatment strategies for these conditions. These neural factors concern the changes in the dopamine function observed in chronic pain and their implication for responses to rewarding stimuli.
2. The dopamine function in chronic pain and in fibromyalgia
2.1 Changes in dopamine function in chronic pain disorders and in FMS
Among the neural changes observed in chronic pain, there is increasing evidence for alterations in the dopamine (DA) system. These changes seem to be related with a reduction of the DA function in chronic pain conditions. Evidence supporting a hypodopaminergic state in chronic pain comes from both preclinical [29] and clinical data [30, 31]. For instance, alterations in the DA function were described in burning mouth syndrome and atypical facial pain [32, 33]. The high incidence of central pain (including neuropathic pain) in patients suffering from Parkinson’s disease suggests that pain is a common symptom in patients with hypofunctional nigrostriatal dopaminergic pathways [34], and that low DA may contribute to increased pain [35]. Similar changes were observed in FMS. For instance, two PET-studies showed that self-reported pain induced by hypertonic saline injection in healthy volunteers correlated with the amount of DA released in the basal ganglia [32, 33]. These findings suggest an involvement of DA activity in endogenous analgesia [36, 37]. In contrast to healthy subjects, FMS patients did not show DA release in response to noxious stimulation [36]. Furthermore, activity of the ventral tegmental area was decreased during both pain perception and expectation of pain relief in FMS patients in an fMRI (functional Magnetic Resonance Imaging) study [30] suggesting a dysregulation of DA signaling in these patients. A previous study by our group added evidence of a reduced DA function in FMS patients, and indicated a role of depression in the relation between pain perception and DA changes [38]. Our main results yielded that investigation of the DA function allows differentiating between FMS patients with and without depression, as well as between FMS patients and healthy subjects and that the neurobiological mechanisms underlying depressive symptoms in FMS patients with depression are different from the ones reported in depressed patients without pain.
Even among healthy individuals, low DA receptor availability has been associated with enhanced pain responses [39]; and DA depletion has been shown to influence pain affect and not the sensory aspects of acute painful stimuli [40]. This could suggest that in chronic pain, a low DA function could lead to changes in affective states [41]. Additionally, recent studies using animal models of neuropathic pain link changes in DA receptor signal transduction, the amount of released DA and other neurochemical adaptations in the midbrain DA circuit with depression-like behaviors and reduced motivation [42, 43, 44]. This is in line with findings showing that aberrant dopaminergic transmission in the mesolimbic DA network underlay several mood disorders [45]. On the other hand, accumulating evidence suggests that the mesolimbic DA system modulates the perception of nociceptive information, and the affective symptoms of chronic pain [46]. Notably, several diseases associated with dysfunctional DA transmission are comorbid with chronic pain, including Parkinson’s disease, drug addiction and major depression [41].
Taken together, there are now multiple lines of evidence showing that chronic pain, including FMS, leads to a hypodopaminergic state that results in enhanced pain sensitivity and might impair motivated behavior [47]. In addition, DA is involved in descending inhibitory modulation of pain transmission, which is an additional link between hypodopaminergia and chronic pain [48]. Strategies to restore dopamine signaling may therefore represent a novel approach to manage pain symptoms in FMS.
2.2 Alterations of the brain reward circuit in chronic pain and FMS
It is well documented that the mesocorticolimbic and mesostriatal DA systems play a role in the processing of reward information [49, 50, 51], even if other neurotransmitter systems, such as the opiate system, are also important in the mediation of reward [52]. Recent studies indicating that alterations of the mesolimbic reward pathway contribute to the pathology of chronic pain [53, 54] suggest a neurobiological overlap between pain processing and the reward circuitry. Pain and reward can be regarded as opponent processes that interact and influence each other [55]. Several studies demonstrated that rewards, including pleasurable stimuli and activities and positive affective states have an analgesic effect and decrease pain sensitivity [55, 56, 57, 58]. Finally, some findings suggest that pain and reward are mediated by similar neural pathways in the central nervous system and that these pathways are related to both the DA and the opioid systems [55, 58]. At a neurochemical level, several preclinical and clinical findings suggest that chronic pain leads to a hypodopaminergic condition in the reward circuitry, resulting in the diminution of the hedonic tone (see Section 2.1). This suggests that the brain reward center might play a key role in the modulation of nociception, and that adaptions in dopaminergic circuitry may affect several sensory and affective components of chronic pain syndromes. These adaptations involve changes in the levels of released DA, as well as postsynaptic changes in the levels of receptors and signal transduction molecules [59].
After having established that pain and reward might influence each other through the implication of the DA system, the next subsections will provide an overview of the findings reporting changes in the responses to reward in chronic pain first, and then secondly specifically in FMS.
2.2.1 Changes in the brain reward circuitry in chronic pain
Findings from functional neuroimaging studies indicate that a network of brain regions, including the orbitofrontal cortex, the ventral (specifically the nucleus accumbens, Nacc) and dorsal striatum, the amygdala and the anterior cingulate gyrus, specifically interact to process reward information [60] and form the so-called cerebral reward system. In chronic pain, alterations in brain structural features, functional connectivity, or activity of these regions have been reported [37, 46, 61]. Additional evidence from clinical studies links chronic pain conditions to aberrant functioning of circuits involved in mood and motivation, including the dopamine brain reward center [62, 63]. The neural changes observed in regions associated with the cerebral reward system could provide a possible explanation for the high incidence of comorbid affective disorders in chronic pain patients [59]. In summary, the reported empirical evidence suggests that pain, in particular chronic pain, impairs several aspects of reward processing: (1) chronic pain is associated with anhedonia, that is, the inability to enjoy pleasurable activities [44, 64]; (2) decreased reward sensitivity and/or decreased motivation was observed in rats with neuropathic pain [65]; (3) impaired operant learning of pain sensitization and habituation was found in FMS patients [66]; and (4) impaired decision making based on reward and punishment was reported in patients with chronic back pain and complex regional pain syndrome (CPRS) [67]. Decreased reward responsivity may therefore underlie a key system mediating anhedonia and depression common with chronic pain [41, 68, 69]. This is highly relevant since the prevalence of depression in chronic pain exceeds 20% [70] and often includes anhedonia [44, 64]. Anhedonia is also one of the cardinal symptoms of depression, and has been hypothesized to be associated with an hypofunction of the DA system, what in turn could affect the neural processing of rewarding information [51]. As a matter of fact, a large body of research has evidenced reduced neural activation as well as reduced DA transmission in response to reward information in patients with major depressive disorder (MDD) (see for instance [71, 72, 73]). Recent evidence from animal studies suggest that suppression of dopaminergic neurotransmission in the mesolimbic reward circuit may be a common neuroplastic change underlying chronic pain and depression that develops in a time-dependent manner [74].
2.2.2 Changes in the brain reward circuitry in FMS
To our knowledge, there is so far only one study that has directly investigated the DA responses to reward in vivo in FMS patients [59]. In this research of our group, we used the [11C]Raclopride positron emission tomography (PET), a radiotracer that is sensitive to changes in intrasynaptic DA concentrations while participants were performing a slot machine compared FMS participants with and without depression with healthy controls (all women). We expected the patients’ groups to have reduced DA responses to reward, expressed as a larger Raclopride binding in the FMS groups of participants than in the group of healthy controls. In addition, we expected this alteration to be stronger in FMS patients with than in FMS patients without depression. However, our results showed, at the contrary of our hypothesis, the greatest [11C]Raclopride displacement in response to rewards in the group of FMS participants with depression [59], which is thought to reflect the largest increase in DA transmission. This can be explained by a greater increase of synaptic DA transmission or by adaptative receptor changes in this group, but necessitate further investigation to be more clearly understood. Our results also indicated that the depression associated with FMS has different neurochemical correlates as primary major depressive disorder. More specifically, a previous study by our group [71] using the same methodology found no [11C]Raclopride displacement in response to rewards in a group of MDD patients without pain symptoms, suggesting reduced DA responses in the brain of depressive patients at the contrary of our group of FMS patients with depression. In conclusion, there is first evidence for a hypodopaminergic state in FMS and an alteration of the neural reactions to reward mediated by the DA system. Even if the exact mechanisms by which the brain reward center modulates chronic pain resp. FMS are not completely established yet, this opens new treatment avenues. Certainly, pharmacological interventions targeting the DA system could be an option. However, we will focus here on psychological interventions that might directly work on the behavioral responses to reward and in turn might be able to restore the DA function.
3. Implications for the treatment of FMS
The current international guidelines for the treatment of FMS all recommend psychological interventions, more specifically cognitive behavioral therapy (CBT), as one of the treatments of choice for the care of FMS patients [22]. According to a recent meta-analysis, CBT is significantly better than the other psychological interventions for which randomized controlled trials exist [75]. The effects of CBT are relatively small but robust and similar to those reported for other pain and drug treatments [75], but have limited success in ameliorating affective and social complaints in FMS patients [25, 75, 76]. There is therefore a need for the development of new CBT methods targeting specific behavioral, emotional or cognitive processes in the treatment of chronic pain. Recently, the so-called “third wave” cognitive-behavior therapies [77] have integrated mindfulness-based cognitive therapy as additional intervention. Mindfulness is defined as “a process of bringing a certain quality of attention to moment-by-moment experience” [78]. Mindfulness capacity can be developed using various meditation techniques that originate from Buddhist spiritual practices [78]. A growing body of research has demonstrated that mindfulness-based interventions are clinically effective for a wide range of problematic conditions (for a review see Grossmann et al. [78]) and have gained increasingly wide use for the treatment of chronic pain conditions including FMS, showing promising results [79, 80]. A recent systematic review indicates a moderate significant effect for mindfulness on the amelioration of mood-related outcomes in FMS [81]. Among these new interventions, Mindfulness-Oriented Recovery Enhancement (MORE) is a mental training program that unites complementary aspects of mindfulness training, CBT and positive psychological principles into an integrative treatment strategy [82]. MORE was originally designed as a behavioral medical intervention for addictive behaviors [83, 84], but was more recently adapted to address chronic pain among individuals receiving long-term opioid analgesic therapy [82]. A randomized clinical trial showed that MORE significantly reduces pain symptoms [82] in chronic pain patients. First empirical evidence suggests that MORE is also associated with behavioral and neurophysiological changes in reward processing [85, 86], suggesting that interventions working on the reward system might be efficient for pain reduction.
4. Conclusion
In conclusion, CBT-based treatments specifically working on the awareness of pleasant experiences, such as MORE, seem to be effective in restoring the behavioral and neural responses to reward and also to diminish pain symptoms in chronic pain patients. Although not yet tested in FMS patients, this could be a promising new treatment alternative for this group of patients, in which changes in the DA function and in the responses to reward have been evidenced, but for whom no efficient treatment is available so far.
Conflicts of interest
There are no conflicts of interests.
\n',keywords:"chronic pain, dopamine, fibromyalgia, depression",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/64845.pdf",chapterXML:"https://mts.intechopen.com/source/xml/64845.xml",downloadPdfUrl:"/chapter/pdf-download/64845",previewPdfUrl:"/chapter/pdf-preview/64845",totalDownloads:504,totalViews:0,totalCrossrefCites:0,dateSubmitted:"August 29th 2018",dateReviewed:"November 16th 2018",datePrePublished:"December 19th 2018",datePublished:null,dateFinished:null,readingETA:"0",abstract:"There have been several indications that pain and reward are partly mediated by similar neural pathways in the central nervous system, and that these common pathways are related to both the dopamine (DA) and the opioid systems. Several studies have demonstrated the analgesic effects of rewarding stimuli or activities on positive affective states. On the other hand, chronic pain was shown to impair several aspects of reward processing by possibly altering pain-reward interactions. However, the precise mechanisms of the mutual pain-reward interaction are unclear and few studies have investigated the influence of pain on rewards and vice versa in humans. Therefore, we aim to summarize recent findings on the neuroanatomical and molecular chances associated with chronic pain conditions, particularly fibromyalgia syndrome (FMS) with a focus on the dopamine system. Recent findings on the mechanisms involved in the alterations of the brain reward circuit in chronic pain and FMS as well as the role of DA in the pathophysiology of FMS and other chronic pain conditions will be discussed. Furthermore, we aim to discuss the interplay between the dopaminergic reward system and depression in chronic pain, as the prevalence of co-morbid depression in chronic pain is quite high.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/64845",risUrl:"/chapter/ris/64845",signatures:"Katharina Ledermann and Chantal Martin-Sölch",book:{id:"8413",title:"Chronic Pain - Physiopathology and Treatment",subtitle:null,fullTitle:"Chronic Pain - Physiopathology and Treatment",slug:null,publishedDate:null,bookSignature:"Ph.D. Vicente Vanaclocha and Dr. Nieves Saiz-Sapena",coverURL:"https://cdn.intechopen.com/books/images_new/8413.jpg",licenceType:"CC BY 3.0",editedByType:null,editors:[{id:"199099",title:"Ph.D.",name:"Vicente",middleName:null,surname:"Vanaclocha",slug:"vicente-vanaclocha",fullName:"Vicente Vanaclocha"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Chronic pain and fibromyalgia",level:"1"},{id:"sec_1_2",title:"1.1 The burden associated with chronic pain",level:"2"},{id:"sec_2_2",title:"1.2 The specific case of Fibromyalgia",level:"2"},{id:"sec_3_2",title:"1.3 Challenges for the treatment of FMS",level:"2"},{id:"sec_5",title:"2. The dopamine function in chronic pain and in fibromyalgia",level:"1"},{id:"sec_5_2",title:"2.1 Changes in dopamine function in chronic pain disorders and in FMS",level:"2"},{id:"sec_6_2",title:"2.2 Alterations of the brain reward circuit in chronic pain and FMS",level:"2"},{id:"sec_6_3",title:"2.2.1 Changes in the brain reward circuitry in chronic pain",level:"3"},{id:"sec_7_3",title:"2.2.2 Changes in the brain reward circuitry in FMS",level:"3"},{id:"sec_10",title:"3. Implications for the treatment of FMS",level:"1"},{id:"sec_11",title:"4. Conclusion",level:"1"},{id:"sec_12",title:"Conflicts of interest",level:"1"}],chapterReferences:[{id:"B1",body:'Merskey H, Bogduk N. 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Proceedings of the National Academy of Sciences of the United States of America. 2009;106(49):20900-20905'},{id:"B58",body:'Borsook D et al. Reward deficiency and anti-reward in pain chronification. Neuroscience and Biobehavioral Reviews. 2016;68:282-297'},{id:"B59",body:'Ledermann K et al. Altered dopamine responses to monetary rewards in female fibromyalgia patients with and without depression: A [11C] raclopride bolus-plus-infusion PET study. Psychotherapy and Psychosomatics. 2017;86(3):181-182'},{id:"B60",body:'O\'Doherty JP. Reward representations and reward-related learning in the human brain: Insights from neuroimaging. Current Opinion in Neurobiology. 2004;14(6):769-776'},{id:"B61",body:'Hashmi JA et al. Shape shifting pain: Chronification of back pain shifts brain representation from nociceptive to emotional circuits. Brain. 2013;136(Pt 9):2751-2768'},{id:"B62",body:'Berryman C et al. Evidence for working memory deficits in chronic pain: A systematic review and meta-analysis. Pain. 2013;154(8):1181-1196'},{id:"B63",body:'Baliki MN, Apkarian AV. Nociception, pain, negative moods, and behavior selection. Neuron. 2015;87(3):474-491'},{id:"B64",body:'Marbach JJ, Richlin DM, Lipton JA. Illness behavior, depression and anhedonia in myofascial face and back pain patients. Psychotherapy and Psychosomatics. 1983;39(1):47-54'},{id:"B65",body:'Ozaki S et al. Suppression of the morphine-induced rewarding effect in the rat with neuropathic pain: Implication of the reduction in mu-opioid receptor functions in the ventral tegmental area. Journal of Neurochemistry. 2002;82(5):1192-1198'},{id:"B66",body:'Becker S et al. Operant conditioning of enhanced pain sensitivity by heat-pain titration. Pain. 2008;140(1):104-114'},{id:"B67",body:'Apkarian AV et al. Chronic pain patients are impaired on an emotional decision-making task. Pain. 2004;108(1-2):129-136'},{id:"B68",body:'Finan PH, Smith MT. The comorbidity of insomnia, chronic pain, and depression: Dopamine as a putative mechanism. Sleep Medicine Reviews. 2013;17(3):173-183'},{id:"B69",body:'Elvemo NA et al. Reward responsiveness in patients with chronic pain. European Journal of Pain. 2015;19(10):1537-1543'},{id:"B70",body:'Wilson KG et al. Major depression and insomnia in chronic pain. The Clinical Journal of Pain. 2002;18(2):77-83'},{id:"B71",body:'Savitz J et al. DRD2/ANKK1 Taq1A polymorphism (rs1800497) has opposing effects on D2/3 receptor binding in healthy controls and patients with major depressive disorder. The International Journal of Neuropsychopharmacology. 2013;16(9):2095-2101'},{id:"B72",body:'Pizzagalli DA et al. Reduced caudate and nucleus accumbens response to rewards in unmedicated individuals with major depressive disorder. The American Journal of Psychiatry. 2009;166(6):702-710'},{id:"B73",body:'Admon R, Pizzagalli DA. Dysfunctional reward processing in depression. Current Opinion in Psychology. 2015;4:114-118'},{id:"B74",body:'Kato T, Ide S, Minami M. Pain relief induces dopamine release in the rat nucleus accumbens during the early but not late phase of neuropathic pain. Neuroscience Letters. 2016;629:73-78'},{id:"B75",body:'Glombiewski JA et al. Psychological treatments for fibromyalgia: A meta-analysis. Pain. 2010;151(2):280-295'},{id:"B76",body:'Sim J, Adams N. Systematic review of randomized controlled trials of nonpharmacological interventions for fibromyalgia. The Clinical Journal of Pain. 2002;18(5):324-336'},{id:"B77",body:'Kahl KG, Winter L, Schweiger U. The third wave of cognitive behavioural therapies: What is new and what is effective? Current Opinion in Psychiatry. 2012;25(6):522-528'},{id:"B78",body:'Grossman P et al. Mindfulness-based stress reduction and health benefits. A meta-analysis. Journal of Psychosomatic Research. 2004;57(1):35-43'},{id:"B79",body:'Veehof MM et al. Acceptance- and mindfulness-based interventions for the treatment of chronic pain: A meta-analytic review. Cognitive Behaviour Therapy. 2016;45(1):5-31'},{id:"B80",body:'Rosenzweig S et al. Mindfulness-based stress reduction for chronic pain conditions: Variation in treatment outcomes and role of home meditation practice. Journal of Psychosomatic Research. 2010;68(1):29-36'},{id:"B81",body:'Theadom A et al. Mind and body therapy for fibromyalgia. Cochrane Database of Systematic Reviews. 2015;4:CD001980'},{id:"B82",body:'Garland EL et al. Mindfulness-oriented recovery enhancement for chronic pain and prescription opioid misuse: Results from an early-stage randomized controlled trial. Journal of Consulting and Clinical Psychology. 2014;82(3):448-459'},{id:"B83",body:'Garland EL et al. Mindfulness training modifies cognitive, affective, and physiological mechanisms implicated in alcohol dependence: Results of a randomized controlled pilot trial. Journal of Psychoactive Drugs. 2010;42(2):177-192'},{id:"B84",body:'Garland EL et al. The downward spiral of chronic pain, prescription opioid misuse, and addiction: Cognitive, affective, and neuropsychopharmacologic pathways. Neuroscience and Biobehavioral Reviews. 2013;37(10 Pt 2):2597-2607'},{id:"B85",body:'Garland EL et al. Restructuring hedonic dysregulation in chronic pain and prescription opioid misuse: Effects of mindfulness-oriented recovery enhancement on responsiveness to drug cues and natural rewards. Psychotherapy and Psychosomatics. 2017;86(2):111-112'},{id:"B86",body:'Garland EL, Froeliger B, Howard MO. Neurophysiological evidence for remediation of reward processing deficits in chronic pain and opioid misuse following treatment with mindfulness-oriented recovery enhancement: Exploratory ERP findings from a pilot RCT. Journal of Behavioral Medicine. 2015;38(2):327-336'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Katharina Ledermann",address:"katharina.ledermann@unifr.ch",affiliation:'
Unit of Clinical and Health Psychology, IReach Lab, University Fribourg, Switzerland
Department of Liaison Psychiatry and Psychosomatics, University Hospital Zurich, University Zurich, Switzerland
Unit of Clinical and Health Psychology, IReach Lab, University Fribourg, Switzerland
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Our business values are based on those any scientist applies to their research. The values of our business are based on the same ones that all good scientists apply to their research. We have created a culture of respect and collaboration within a relaxed, friendly, and progressive atmosphere, while maintaining academic rigour.
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Integrity - We are consistent and dependable, always striving for precision and accuracy in the true spirit of science.
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Disruptiveness - We are eager for discovery, for new ideas and for progression. We approach our work with creativity and determination, with a clear vision that drives us forward. We look beyond today and strive for a better tomorrow.
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IntechOpen is a dynamic, vibrant company, where exceptional people are achieving great things. We offer a creative, dedicated, committed, and passionate environment but never lose sight of the fact that science and discovery is exciting and rewarding. We constantly strive to ensure that members of our community can work, travel, meet world-renowned researchers and grow their own career and develop their own experiences.
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If this sounds like a place that you would like to work, whether you are at the beginning of your career or are an experienced professional, we invite you to drop us a line and tell us why you could be the right person for IntechOpen.
Integrity - We are consistent and dependable, always striving for precision and accuracy in the true spirit of science.
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Openness - We communicate honestly and transparently. We are open to constructive criticism and committed to learning from it.
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Disruptiveness - We are eager for discovery, for new ideas and for progression. We approach our work with creativity and determination, with a clear vision that drives us forward. We look beyond today and strive for a better tomorrow.
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What makes IntechOpen a great place to work?
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IntechOpen is a dynamic, vibrant company, where exceptional people are achieving great things. We offer a creative, dedicated, committed, and passionate environment but never lose sight of the fact that science and discovery is exciting and rewarding. We constantly strive to ensure that members of our community can work, travel, meet world-renowned researchers and grow their own career and develop their own experiences.
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If this sounds like a place that you would like to work, whether you are at the beginning of your career or are an experienced professional, we invite you to drop us a line and tell us why you could be the right person for IntechOpen.
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